Automatic Vehicle Recognition (AVR) is a term applied to the detection and recognition of a vehicle by an electronic system. AVR systems, including, for example, electronic toll systems, red light running systems, speed enforcement systems, and access control systems, are becoming more prevalent. Ideal AVR systems are able to read all vehicles with 100% accuracy. The two main types of AVR systems in use today are (1) systems using RFID technology to read an RFID tag attached to a vehicle and (2) systems using a machine or device to read a machine readable code attached to a vehicle.
One advantage of RFID systems is their high accuracy, which is achieved by virtue of error detection and correction information contained on the RFID tag. Using well known mathematical techniques (cyclic redundancy check, or CRC, for example), the probability that a read is accurate (or the inverse) can be determined. However, RFID systems have some disadvantages, including that not all vehicles include RFID tags. Also, existing “passive” RFID systems (unpowered RFID tags plus readers) cannot pinpoint the exact location of a tagged object. Rather, they simply report the presence or absence of a tag in their field of sensitivity. Moreover, many passive RFID systems only operate at short range, function poorly in the presence of metal, and are blocked by interference when many tagged objects are present. Some of these problems can be overcome by using active RFID technology or similar methods. However, these techniques require expensive, power-consuming electronics and batteries, and they still may not determine position accurately when attached to dense or metallic objects.
Machine vision systems (often called Automated License Plate Readers or ALPR systems) use a machine or device to read a machine readable code attached to a vehicle. In many embodiments, the machine readable code is attached to, printed on, or adjacent to a license plate. An optimal ALPR system must be able to accurately read all license plates that may pass through the system. One advantage of ALPR systems is that they are can be used almost universally, since almost all areas of the world require that vehicles have license plates with visually identifiable information thereon. However, the task of recognizing visual tags can be complicated. For example, the read accuracy from an ALPR system is largely dependent on the quality of the captured image as assessed by the reader. Accurate reading of a vehicle's license plate is becoming increasingly difficult for a variety of reasons one of which is the wide variety of license plates now on the roads.
Many licensing authorities offer “specialty” license plates. Such plates allow the driver to select an attractive or meaningful design that will be printed on their license plate. The production of such “specialty” license plates results in each state offering numerous different license plate designs to its constituents. The state of Oregon, for example, offers seven different license plate design options to standard vehicle drivers: tree, salmon, Crater Lake, cultural trust, amateur radio operator (ham), antique vehicle, and special interest. Also, the state offers six types of non-profit plates to standard vehicle drivers: Lions Club, Oregon Masonic Family, Oregon Professional Firefighters, Oregon State Elks, Share the Road, and Support Our Troops plates. Additionally, the state offers six types of high education plates to drivers of standard vehicles: Eastern Oregon University, Oregon State University, Portland State University, University of Oregon, University of Portland, and Willamette University. Further, the state offers ten veteran and service-related plates to drivers of standard vehicles: Congressional Medal of Honor; Disabled Veteran; Ex-POW; First Marine Division; Gold Star Family; National Guard; Non-Commission Officers Association; Purple Heart; Veterans Recognition; and Vietnam Veterans. This results in a total of 33 different personalized plate options for standard vehicles in a single state. If each of the 50 states and each of the 10 Canadian provinces offered approximately the same number of options for standard vehicles, almost 2000 different design options for license plates would be available. This does not even take into account the license plate options for mopeds, motorcycles, campers, trailers, trucks, commercial vehicles, government vehicles, dealer vehicles, and motor homes. Also, each license plate type may use a different font, and font misinterpretation is a common error in ALPR systems.
Another reason why accurate license plate reading is challenging is that license plates naturally get dirty. ALPR systems often rely on optical identification of the alphanumerics on a license plate in order to accurately read the license plate. When these alphanumerics are dirty, they become obscured and their visibility and clarity is significantly compromised, often resulting in inaccurate license plate reads. Additionally, existing ALPR systems have difficulty distinguishing the machine readable code from complex backgrounds and in variable lighting conditions.
One exemplary ALPR system includes a bar code containing “an identification code which will provide information about the vehicle,” as is described in PCT Publication No. 2008/007076 to Retainagroup Ltd. Some publications (e.g., European Patent Publication No. 0416742 and U.S. Pat. No. 6,832,728) discuss including one or more of owner information, serial numbers, vehicle type, vehicle weight, plate number, state, plate type, and county on a machine readable portion of a license plate.